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We consider the multidimensional knapsack problem (MKP) with min-max regret criterion under interval profits. We examine three typical algorithms widely applied for min-max regret criterion: fixed-scenario approach, Benders-like decomposition and branch-and-cut. We further propose a new heuristic framework, which we call the iterated dual substitution algorithm. Computational experiments on a wide...
Robust design optimization aims to find solutions that are competent and reliable under given uncertainties. While such uncertainties can emerge from a number of sources (imprecise variable values, errors in performance estimates, varying environmental conditions, etc.), this paper focuses on problems where uncertainties emanate from design variables. In commercial designs, being reliable is often...
In this paper, we propose a novel randomized approach to Stochastic Model Predictive Control (SMPC) for a linear system affected by a disturbance with unbounded support. As it is common in this setup, we focus on the case where the input/state of the system are subject to probabilistic constraints, i.e., the constraints have to be satisfied for all the disturbance realizations but for a set having...
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